Generative Adversarial Nets

Generative Adversarial Networks (GANs) are a framework for training generative models. Two neural networks contest with each other in a game. A generator network learns to create data that resembles the training data, while a discriminator network learns to distinguish between the generated data and the real training data. By pitting these networks against each other, both networks improve, leading to a generative model that can produce realistic samples. This approach has been highly influential in various AI applications, especially in image and video generation, and is fundamental to many AI-powered graphic design tools.